This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
課程信息
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學習Course的學生是
- Data Scientists
- User Experience Researchers
- Data Analysts
- Machine Learning Engineers
- Librarians
您將獲得的技能
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
教學大綱 - 您將從這門課程中學到什麼
週
1完成時間為 2 小時
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 個視頻 （總計 15 分鐘）, 5 個閱讀材料, 2 個測驗
5 個閱讀材料
Welcome to Text Mining and Analytics!10分鐘
Syllabus15分鐘
About the Discussion Forums15分鐘
Updating your Profile10分鐘
Social Media10分鐘
2 個練習
Orientation Quiz15分鐘
Pre-Quiz26分鐘
完成時間為 4 小時
Week 1
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
9 個視頻 （總計 109 分鐘）, 1 個閱讀材料, 2 個測驗
9 個視頻
1.2 Overview Text Mining and Analytics: Part 211分鐘
1.3 Natural Language Content Analysis: Part 112分鐘
1.4 Natural Language Content Analysis: Part 24分鐘
1.5 Text Representation: Part 110分鐘
1.6 Text Representation: Part 29分鐘
1.7 Word Association Mining and Analysis15分鐘
1.8 Paradigmatic Relation Discovery Part 114分鐘
1.9 Paradigmatic Relation Discovery Part 217分鐘
1 個閱讀材料
Week 1 Overview10分鐘
2 個練習
Week 1 Practice Quiz1小時
Week 1 Quiz1小時
週
2完成時間為 4 小時
Week 2
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
10 個視頻 （總計 116 分鐘）, 1 個閱讀材料, 2 個測驗
10 個視頻
2.2 Syntagmatic Relation Discovery: Conditional Entropy11分鐘
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113分鐘
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29分鐘
2.5 Topic Mining and Analysis: Motivation and Task Definition7分鐘
2.6 Topic Mining and Analysis: Term as Topic11分鐘
2.7 Topic Mining and Analysis: Probabilistic Topic Models14分鐘
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110分鐘
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213分鐘
2.10 Probabilistic Topic Models: Mining One Topic12分鐘
1 個閱讀材料
Week 2 Overview10分鐘
2 個練習
Week 2 Practice Quiz1小時
Week 2 Quiz1小時
週
3完成時間為 10 小時
Week 3
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
10 個視頻 （總計 103 分鐘）, 2 個閱讀材料, 3 個測驗
10 個視頻
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110分鐘
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28分鐘
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111分鐘
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210分鐘
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36分鐘
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110分鐘
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210分鐘
3.9 Latent Dirichlet Allocation (LDA): Part 110分鐘
3.10 Latent Dirichlet Allocation (LDA): Part 212分鐘
2 個閱讀材料
Week 3 Overview10分鐘
Programming Assignments Overview10分鐘
2 個練習
Week 3 Practice Quiz1小時
Quiz: Week 3 Quiz1小時
週
4完成時間為 5 小時
Week 4
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
9 個視頻 （總計 141 分鐘）, 1 個閱讀材料, 2 個測驗
9 個視頻
4.2 Text Clustering: Generative Probabilistic Models Part 116分鐘
4.3 Text Clustering: Generative Probabilistic Models Part 28分鐘
4.4 Text Clustering: Generative Probabilistic Models Part 314分鐘
4.5 Text Clustering: Similarity-based Approaches17分鐘
4.6 Text Clustering: Evaluation10分鐘
4.7 Text Categorization: Motivation14分鐘
4.8 Text Categorization: Methods11分鐘
4.9 Text Categorization: Generative Probabilistic Models31分鐘
1 個閱讀材料
Week 4 Overview10分鐘
2 個練習
Week 4 Practice Quiz1小時
Week 4 Quiz1小時
4.4
102 個審閱33%
完成這些課程後已開始新的職業生涯
42%
通過此課程獲得實實在在的工作福利
12%
加薪或升職
來自文本挖掘和分析的熱門評論
Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.
The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.
講師
ChengXiang Zhai
ProfessorDepartment of Computer Science
立即開始攻讀碩士學位
此 課程 隸屬於 伊利诺伊大学香槟分校 提供的 100% 在線 Master in Computer Science。如果您被錄取參加全部課程，您的課程將計入您的學位學習進程。
關於 伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
關於 数据挖掘 專項課程
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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